%0 Book Section
%3 Paper 1_Metaheuristics_Oficial.pdf
%4 sid.inpe.br/plutao/2022/06.15.13.10.47
%A Santiago Júnior, Valdivino Alexandre de,
%A Sales, Camila Pereira,
%@secondarytype PRE LI
%B Proceedings of International Joint Conference on Advances in Computational Intelligence
%C Singapore
%D 2022
%E Uddin, M. S. ,
%E Jamwal, P. K. ,
%E Bansal, J. C.,
%F lattes: 5039690360728170 1 SantiagoJúniorSale:2022:MeHyBa
%I Springer Nature Singapore
%K Metaheuristics, Hyper-heuristics, Software Integration Testing, Controlled Experiment, Optimisation.
%P 131-151
%T Metaheuristics and Hyper-heuristics Based on Evolutionary Algorithms for Software Integration Testing
%V 1
%X Hyper-heuristics have been identified as optimisation algorithms that would have better generalisation capabilities than metaheuristics. In this article, we present a controlled experiment that evaluates four metaheuristics (evolutionary algorithms), two multi-objective (SPEA2, IBEA) and two many-objective (NSGA-III, MOMBI-II), and three selection hyper-heuristics (HRISE_R, HRISE_M, Choice Function) for the software integration testing problem. We relied on and improved our previous method which aims at generating integration test cases based on C++ source code and optimisation algorithms. Considering three different quality indicators and two types of evaluations (cross-domain and statistical analyses), results demonstrate that, for the algorithms and case studies considered in this research, classical metaheuristics, such as SPEA2 and IBEA, performed better compared to not only the most recent many-objective algorithms but also to the hyper-heuristics. This conclusion, based on empirical evidences, seems to be related to the well-known no free lunch theorems which assert that any two algorithms are equivalent when their performances are averaged across all possible problems. Hence, we claim that it is needed to carry out more rigorous experiments, in the context of optimisation, to better answer the question of generalisation in practical terms.
%@area COMP
%@electronicmailaddress valdivino.santiago@inpe.br  
%@electronicmailaddress camilapsales27@gmail.com
%@group COPDT-CGIP-INPE-MCTI-GOV-BR
%@group CAP-COMP-DIPGR-INPE-MCTI-GOV-BR
%@isbn 9789811903
%@usergroup lattes  
%@usergroup self-uploading-INPE-MCTI-GOV-BR
%@resumeid 8JMKD3MGP5W/3C9JJB5
%@nexthigherunit 8JMKD3MGPCW/3F2PHGS
%@nexthigherunit 8JMKD3MGPCW/46KUES5
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@versiontype publisher
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@doi 10.1007/978-981-19-0332-8_10
%2 sid.inpe.br/plutao/2022/06.15.13.10.48